Entropy-like Divergence Based Kernel Fuzzy Clustering for Robust Image Segmentation

Wu, Chengmao; Cao, Zhuo

doi:10.1016/j.eswa.2020.114327

Cited by 17 publications

(3 citation statements)

References 33 publications

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“…V RE is the error rate of data reconstruction, which is defined as follows: (31) It analyzes the difference between reconstructed data and the original data after clustering.…”

Section: Analysis Of Weighting Exponent M In Fcmmentioning

confidence: 99%

“…The learning process of the fuzzy clustering algorithm depends on the metric that describes associations between different objects. Fuzzy c-means (FCM) uses Euclidean distance to measure relationship between samples and distance centers [27]; SFCM measures relationship between data samples, distance centers and guidance information by using Euclidean distance [28]; eSFCM measures data samples and cluster centers and entropy to measure guidance information by using Euclidean distance [29]; SMUC measures relationship between samples and distance centers and entropy to measure guidance information by using Mahalanobis distance [30]; In addition, there are some kernel methods [31] [32]. However, the learning process of the relationship between data samples will be affected due to the limitations of metrics.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Metric Accelerated Method for Fuzzy Clustering

Cai

Xia

et al. 2021

IEEE Access

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“…V RE is the error rate of data reconstruction, which is defined as follows: (31) It analyzes the difference between reconstructed data and the original data after clustering.…”

Section: Analysis Of Weighting Exponent M In Fcmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Metric Accelerated Method for Fuzzy Clustering

Cai

Xia

et al. 2021

IEEE Access

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“…As a primary and available method, Otsu has long been highly valued and applied [ 19 ]. In addition, methods based on information entropy are extensively concerned due to captivating mathematical concepts, such as Shannon entropy [ 20 ], fuzzy entropy [ 21 ], Tsallis entropy [ 22 ], Renyi entropy [ 23 ], and Kapur entropy [ 24 ]. Among them, Kapur entropy classifies an image into multiple classes by comparing the entropy of the histogram.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Zhu

Cao

et al. 2021

Entropy

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Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

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